[fix] model support stream

api/app/core/models/llm.py

@@ -1,8 +1,8 @@
 from __future__ import annotations
-from typing import Any, Dict, List, Optional
+from typing import Any, Iterator, AsyncIterator, List, Optional
 from langchain_core.callbacks import CallbackManagerForLLMRun, AsyncCallbackManagerForLLMRun
 from langchain_core.language_models import BaseLLM
-from langchain_core.outputs import LLMResult
+from langchain_core.outputs import LLMResult, GenerationChunk
 
 from app.core.models import RedBearModelConfig, RedBearModelFactory, get_provider_llm_class
 from app.models.models_model import ModelType
@@ -10,21 +10,36 @@ from app.models.models_model import ModelType
 
 class RedBearLLM(BaseLLM):
     """
-    RedBear LLM 模型包装器 - 完全动态代理实现
+    RedBear LLM Model Wrapper
     
-    这个包装器自动将所有方法调用委托给内部模型，
-    同时提供优雅的回退机制和错误处理。
+    This wrapper provides a unified interface to access different LLM providers,
+    while maintaining all LangChain functionality, including streaming output.
+    
+    Features:
+    - Support for multiple LLM providers (OpenAI, Qwen, Ollama, etc.)
+    - Full streaming output support
+    - Elegant error handling and fallback mechanism
+    - Automatic proxying of all underlying model methods and attributes
     """
 
-    def __init__(self, config: RedBearModelConfig, type: ModelType=ModelType.LLM):
-        self._model = self._create_model(config, type)
+    def __init__(self, config: RedBearModelConfig, type: ModelType = ModelType.LLM):
+        """Initialize RedBear LLM wrapper
+        
+        Args:
+            config: Model configuration
+            type: Model type (LLM or CHAT)
+        """
+        super().__init__()
         self._config = config
+        self._model = self._create_model(config, type)
 
     @property
     def _llm_type(self) -> str:
-        """返回LLM类型标识符"""
-        return self._model._llm_type
+        """Return LLM type identifier"""
+        return getattr(self._model, '_llm_type', 'redbear_llm')
 
+    # ==================== Core Methods (Required by BaseLLM) ====================
+    
     def _generate(
         self,
         prompts: List[str],
@@ -32,7 +47,7 @@ class RedBearLLM(BaseLLM):
         run_manager: Optional[CallbackManagerForLLMRun] = None,
         **kwargs: Any
     ) -> LLMResult:
-        """同步生成文本"""
+        """Synchronous text generation (required by BaseLLM)"""
         return self._model._generate(prompts, stop=stop, run_manager=run_manager, **kwargs)
 
     async def _agenerate(
@@ -42,92 +57,233 @@ class RedBearLLM(BaseLLM):
         run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
         **kwargs: Any
     ) -> LLMResult:
-        """异步生成文本"""
+        """Asynchronous text generation (required by BaseLLM)"""
         return await self._model._agenerate(prompts, stop=stop, run_manager=run_manager, **kwargs)
 
-    # 关键：覆盖 invoke/ainvoke，直接委托到底层模型，避免 BaseLLM 的字符串化行为
+    # ==================== Advanced Methods (Support Message Lists) ====================
+    
     def invoke(self, input: Any, config: Optional[dict] = None, **kwargs: Any) -> Any:
-        """直接调用底层模型以支持 ChatPrompt 和消息列表。"""
+        """Synchronous model invocation
+        
+        Supports various input formats including strings and message lists.
+        Directly delegates to the underlying model to avoid BaseLLM's string conversion.
+        
+        Args:
+            input: Input (string, message list, etc.)
+            config: Runtime configuration
+            **kwargs: Additional arguments
+            
+        Returns:
+            Model response
+        """
         try:
             return self._model.invoke(input, config=config, **kwargs)
         except AttributeError as e:
-            # 只在属性错误时回退（说明底层模型不支持该方法）
             if 'invoke' in str(e):
+                # Underlying model doesn't support invoke, fallback to parent implementation
                 return super().invoke(input, config=config, **kwargs)
-            # 其他 AttributeError 直接抛出
             raise
         except Exception:
-            # 其他所有异常（包括 ValidationException）直接抛出，不回退
+            # Other exceptions are raised directly
             raise
 
     async def ainvoke(self, input: Any, config: Optional[dict] = None, **kwargs: Any) -> Any:
-        """异步直接调用底层模型以支持 ChatPrompt 和消息列表。"""
+        """Asynchronous model invocation
+        
+        Supports various input formats including strings and message lists.
+        Directly delegates to the underlying model to avoid BaseLLM's string conversion.
+        
+        Args:
+            input: Input (string, message list, etc.)
+            config: Runtime configuration
+            **kwargs: Additional arguments
+            
+        Returns:
+            Model response
+        """
         try:
             return await self._model.ainvoke(input, config=config, **kwargs)
         except AttributeError as e:
-            # 只在属性错误时回退（说明底层模型不支持该方法）
             if 'ainvoke' in str(e):
+                # Underlying model doesn't support ainvoke, fallback to parent implementation
                 return await super().ainvoke(input, config=config, **kwargs)
-            # 其他 AttributeError 直接抛出
             raise
         except Exception:
-            # 其他所有异常（包括 ValidationException）直接抛出，不回退
+            # Other exceptions are raised directly
             raise
 
-    def __getattr__(self, name):
-        """
-        动态代理：将所有未定义的属性和方法调用委托给内部模型
+    # ==================== Streaming Methods (Critical) ====================
+    
+    def stream(
+        self,
+        input: Any,
+        config: Optional[dict] = None,
+        *,
+        stop: Optional[List[str]] = None,
+        **kwargs: Any
+    ) -> Iterator[GenerationChunk]:
+        """Synchronous streaming model invocation
         
-        这是最优雅的包装器实现方式，完全避免了方法重复定义
-        """
-        # 处理特殊属性以避免递归
-        if name in ('__isabstractmethod__', '__dict__', '__class__'):
-            raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
+        Args:
+            input: Input (string, message list, etc.)
+            config: Runtime configuration
+            stop: List of stop words
+            **kwargs: Additional arguments
             
-        # 检查内部模型是否有该属性（使用安全的方式避免递归）
+        Yields:
+            GenerationChunk: Generated text chunks
+        """
+        try:
+            yield from self._model.stream(input, config=config, stop=stop, **kwargs)
+        except AttributeError as e:
+            if 'stream' in str(e):
+                # Underlying model doesn't support stream, fallback to parent implementation
+                yield from super().stream(input, config=config, stop=stop, **kwargs)
+            else:
+                raise
+        except Exception:
+            raise
+    
+    async def astream(
+        self,
+        input: Any,
+        config: Optional[dict] = None,
+        *,
+        stop: Optional[List[str]] = None,
+        **kwargs: Any
+    ) -> AsyncIterator[GenerationChunk]:
+        """Asynchronous streaming model invocation
+        
+        This is the core method for streaming output. It directly proxies to the
+        underlying model's astream method, maintaining generator characteristics
+        to ensure each chunk is delivered in real-time.
+        
+        Args:
+            input: Input (string, message list, etc.)
+            config: Runtime configuration
+            stop: List of stop words
+            **kwargs: Additional arguments
+            
+        Yields:
+            GenerationChunk: Generated text chunks
+        """
+        try:
+            async for chunk in self._model.astream(input, config=config, stop=stop, **kwargs):
+                yield chunk
+        except AttributeError as e:
+            if 'astream' in str(e):
+                # Underlying model doesn't support astream, fallback to parent implementation
+                async for chunk in super().astream(input, config=config, stop=stop, **kwargs):
+                    yield chunk
+            else:
+                raise
+        except Exception:
+            raise
+
+    # ==================== Dynamic Proxy ====================
+    
+    def __getattr__(self, name: str) -> Any:
+        """Dynamic proxy: delegate undefined attributes and method calls to internal model
+        
+        This method allows RedBearLLM to transparently access all attributes and methods
+        of the underlying model without explicitly defining each one.
+        
+        Args:
+            name: Attribute or method name
+            
+        Returns:
+            Attribute value or method
+            
+        Raises:
+            AttributeError: If attribute doesn't exist
+        """
+        # Avoid recursion: raise error directly for special attributes
+        if name in ('__isabstractmethod__', '__dict__', '__class__', '_model', '_config'):
+            raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
+        
+        # Try to get attribute from internal model
         try:
-            # 使用 object.__getattribute__ 来安全地检查内部模型的属性
             attr = object.__getattribute__(self._model, name)
             
-            # 如果是方法，返回一个包装器来处理调用
+            # If it's callable (a method)
             if callable(attr):
-                # 流式方法直接返回，不包装（保持生成器特性）
-                if name in ('_stream', '_astream', 'stream', 'astream'):
+                # Streaming methods are returned directly to maintain generator characteristics
+                # Note: Although we've explicitly implemented stream/astream,
+                # this is kept to handle internal methods like _stream/_astream
+                if name in ('_stream', '_astream'):
                     return attr
                 
-                # 非流式方法使用包装器处理异常
+                # Wrap other methods for easier debugging and error handling
                 def method_wrapper(*args, **kwargs):
-                    return attr(*args, **kwargs)
+                    try:
+                        return attr(*args, **kwargs)
+                    except Exception:
+                        # Can add logging or error handling here
+                        raise
                 
-                # 保持方法的元信息
+                # Preserve method metadata
                 method_wrapper.__name__ = name
                 method_wrapper.__doc__ = getattr(attr, '__doc__', f"Delegated method: {name}")
                 return method_wrapper
             
-            # 如果是普通属性，直接返回
+            # If it's a regular attribute, return directly
             return attr
             
         except AttributeError:
-            # 内部模型没有该属性，尝试回退实现
+            # Internal model doesn't have this attribute either
             pass
         
-        # 检查是否有回退方法（使用安全的方式避免递归）
+        # Check if there's a fallback method
         fallback_name = f'_fallback_{name}'
         try:
-            fallback_method = object.__getattribute__(self, fallback_name)
-            return fallback_method
+            return object.__getattribute__(self, fallback_name)
         except AttributeError:
-            # 没有回退方法，抛出适当的错误
             pass
         
-        # 如果都没有，抛出适当的错误
-        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
+        # Nothing found, raise error
+        raise AttributeError(
+            f"'{type(self).__name__}' object has no attribute '{name}'. "
+            f"The underlying model '{type(self._model).__name__}' also doesn't have this attribute."
+        )
 
+    # ==================== Helper Methods ====================
+    
     def _create_model(self, config: RedBearModelConfig, type: ModelType) -> BaseLLM:
-        """创建内部模型实例"""
+        """Create internal model instance
+        
+        Args:
+            config: Model configuration
+            type: Model type
+            
+        Returns:
+            Created model instance
+        """
         llm_class = get_provider_llm_class(config, type)
         model_params = RedBearModelFactory.get_model_params(config)
         return llm_class(**model_params)
-
-
-   
+    
+    def get_config(self) -> RedBearModelConfig:
+        """Get model configuration
+        
+        Returns:
+            Model configuration object
+        """
+        return self._config
+    
+    def get_underlying_model(self) -> BaseLLM:
+        """Get underlying model instance
+        
+        Returns:
+            Underlying model instance
+        """
+        return self._model
+    
+    def __repr__(self) -> str:
+        """Return string representation of the object"""
+        return (
+            f"RedBearLLM("
+            f"provider={self._config.provider}, "
+            f"model={self._config.model_name}, "
+            f"type={type(self._model).__name__}"
+            f")"
+        )

api/app/core/workflow/executor.py

@@ -125,17 +125,22 @@ class WorkflowExecutor:
                 if stream:
                     # 流式模式：创建 async generator 函数
                     # LangGraph 会收集所有 yield 的值，最后一个 yield 的字典会被合并到 state
-                    async def node_func(state: WorkflowState, inst=node_instance):
-                        async for item in inst.run_stream(state):
-                            yield item
-                    workflow.add_node(node_id, node_func)
+                    def make_stream_func(inst):
+                        async def node_func(state: WorkflowState):
+                            # logger.debug(f"流式执行节点: {inst.node_id}, 支持流式: {inst.supports_streaming()}")
+                            async for item in inst.run_stream(state):
+                                yield item
+                        return node_func
+                    workflow.add_node(node_id, make_stream_func(node_instance))
                 else:
                     # 非流式模式：创建 async function
-                    async def node_func(state: WorkflowState, inst=node_instance):
-                        return await inst.run(state)
-                    workflow.add_node(node_id, node_func)
+                    def make_func(inst):
+                        async def node_func(state: WorkflowState):
+                            return await inst.run(state)
+                        return node_func
+                    workflow.add_node(node_id, make_func(node_instance))
 
-                logger.debug(f"添加节点: {node_id} (type={node_type})")
+                logger.debug(f"添加节点: {node_id} (type={node_type}, stream={stream})")
 
         # 3. 添加边
         # 从 START 连接到 start 节点
@@ -283,13 +288,9 @@ class WorkflowExecutor:
     ):
         """执行工作流（流式）
 
-        使用 stream_mode="updates" 来获取每个节点的 state 更新。
-        节点的 generator 会 yield 多个值：
-        - 中间的 chunk 事件（带 type="chunk"）
-        - 最后的 state 更新（纯字典，包含 node_outputs 等）
-
-        LangGraph 会将所有 yield 的值收集起来，并将它们合并到 state 中。
-        我们需要过滤出 chunk 事件并转发，同时确保 state 更新被正确处理。
+        使用多个 stream_mode 来获取：
+        1. "updates" - 节点的 state 更新和流式 chunk
+        2. "debug" - 节点执行的详细信息（开始/完成时间）
 
         Args:
             input_data: 输入数据
@@ -297,7 +298,7 @@ class WorkflowExecutor:
         Yields:
             流式事件
         """
-        logger.info(f"开始执行工作流: execution_id={self.execution_id}")
+        logger.info(f"开始执行工作流（流式）: execution_id={self.execution_id}")
 
         # 记录开始时间
         start_time = datetime.datetime.now()
@@ -310,34 +311,73 @@ class WorkflowExecutor:
 
         # 3. 执行工作流
         try:
-            async for mode, event in graph.astream(
+            chunk_count = 0
+            async for event in graph.astream(
                     initial_state,
-                    stream_mode=["updates","messages"],
+                    stream_mode=["updates", "debug"],
             ):
-                # print("刚才跑的节点:", event[0])
-                # # 通过图结构就能算出“接下来是谁”
-                # print("接下来可能跑:", graph.get_next(event[0]))
-                # print("="*50)
-                # # print("mode",mode)
-                # print("event",event)
-                # print("="*50)
-                # event 是一个字典，key 是节点 ID，value 是 state 更新或 chunk
-                for node_id, update in event.items():
-                    print("="*50)
-                    print("node_id",node_id)
-                    print("update",update)
+                mode, data = event
 
-                    print("="*50)
-                    if isinstance(update, dict) and update.get("type") == "chunk":
-                        # 这是流式 chunk，转发给客户端
+                if mode == "debug":
+                    # 处理调试信息（节点执行状态）
+                    event_type = data.get("type")
+                    payload = data.get("payload", {})
+                    node_name = payload.get("name")
+
+                    if event_type == "task":
+                        # 节点开始执行
+                        inputv = payload.get("input", {})
+                        variables = inputv.get("variables", {})
+                        variables_sys = variables.get("sys", {})
+                        conversation_id = variables_sys.get("conversation_id")
+                        execution_id = variables_sys.get("execution_id")
+                        logger.info(f"[DEBUG] 节点开始执行: {node_name}")
                         yield {
-                            "type": "node_chunk",
-                            "node_id": update.get("node_id"),
-                            "chunk": update.get("content")
+                            "type": "node_start",
+                            "node_id": node_name,
+                            "conversation_id": conversation_id,
+                            "execution_id": execution_id,
+                            "timestamp": data.get("timestamp")
                         }
-                    # 其他情况（state 更新）会被 LangGraph 自动合并到 state，不需要我们处理
-                print(event)
-                yield event
+                    elif event_type == "task_result":
+                        # 节点执行完成
+                        result = payload.get("result", {})
+                        inputv = result.get("input", {})
+                        variables = inputv.get("variables", {})
+                        variables_sys = variables.get("sys", {})
+                        conversation_id = variables_sys.get("conversation_id")
+                        execution_id = variables_sys.get("execution_id")
+                        logger.info(f"[DEBUG] 节点执行完成: {node_name}")
+                        yield {
+                            "type": "node_end",
+                            "node_id": node_name,
+                            "conversation_id": conversation_id,
+                            "execution_id": execution_id,
+                            "timestamp": data.get("timestamp")
+                        }
+
+                elif mode == "updates":
+                    # 处理 state 更新
+                    # data 是一个字典，key 是节点 ID，value 是 state 更新或 chunk
+                    print("="*50)
+                    print(data)
+                    print("-"*50)
+                    for node_id, update in data.items():
+                        if isinstance(update, dict) and update.get("type") == "chunk":
+                            # 这是流式 chunk，转发给客户端
+                            chunk_count += 1
+                            logger.debug(f"[UPDATE] 收到 chunk #{chunk_count} from {node_id}: {update.get('content')[:50]}...")
+                            yield {
+                                "type": "node_chunk",
+                                "node_id": update.get("node_id"),
+                                "chunk": update.get("content"),
+                                "full_content": update.get("full_content")
+                            }
+                        else:
+                            logger.debug(f"[UPDATE] 收到 state 更新 from {node_id}")
+                        # 其他情况（state 更新）会被 LangGraph 自动合并到 state
+            
+            logger.info(f"工作流执行完成（流式），总 chunks: {chunk_count}")
 
         except Exception as e:
             # 计算耗时（即使失败也记录）

api/app/core/workflow/nodes/base_node.py

@@ -245,6 +245,9 @@ class BaseNode(ABC):
                         final_result = item["result"]
                     elif isinstance(item, str):
                         # 字符串是 chunk
+                        # print("="*50)
+                        # print(item)
+                        # print("-"*50)
                         chunks.append(item)
                         yield {
                             "type": "chunk",

api/app/core/workflow/nodes/end/node.py

@@ -30,11 +30,7 @@ class EndNode(BaseNode):
         
         # 获取配置的输出模板
         output_template = self.config.get("output")
-        # pool = self.get_variable_pool(state)
-       
-        # print("="*20)
-        # print( pool.get("start.test"))
-        # print("="*20)
+        
         # 如果配置了输出模板，使用模板渲染；否则使用默认输出
         if output_template:
             output = self._render_template(output_template, state)
@@ -46,7 +42,45 @@ class EndNode(BaseNode):
         total_nodes = len(node_outputs)
         
         logger.info(f"节点 {self.node_id} (End) 执行完成，共执行 {total_nodes} 个节点")
-        print("="*20)
-        print(output)
-        print("="*20)
+        
         return output
+    
+    async def execute_stream(self, state: WorkflowState):
+        """流式执行 end 节点业务逻辑
+        
+        当 end 节点前面是 LLM 节点时，流式输出其内容。
+        
+        Args:
+            state: 工作流状态
+        
+        Yields:
+            文本片段（chunk）或完成标记
+        """
+        logger.info(f"节点 {self.node_id} (End) 开始执行（流式）")
+        
+        # 获取配置的输出模板
+        output_template = self.config.get("output")
+        
+        # 如果配置了输出模板，使用模板渲染
+        if output_template:
+            output = self._render_template(output_template, state)
+            
+            # 检查输出中是否包含节点引用（如 {{llm_node.output}}）
+            # 如果包含，则逐字符流式输出
+            if output:
+                # 逐字符流式输出
+                for char in output:
+                    yield char
+        else:
+            output = "工作流已完成"
+            for char in output:
+                yield char
+        
+        # 统计信息（用于日志）
+        node_outputs = state.get("node_outputs", {})
+        total_nodes = len(node_outputs)
+        
+        logger.info(f"节点 {self.node_id} (End) 执行完成（流式），共执行 {total_nodes} 个节点")
+        
+        # yield 完成标记
+        yield {"__final__": True, "result": output}

api/app/core/workflow/nodes/llm/node.py

@@ -125,19 +125,22 @@ class LLMNode(BaseNode):
             model_type = config.type
         
         # 4. 创建 LLM 实例（使用已提取的数据）
-        print("="*50)
-        print("stream",stream)
-        print("="*50)
+        # 注意：对于流式输出，需要在模型初始化时设置 streaming=True
+        extra_params = {"streaming": stream} if stream else {}
+        
         llm = RedBearLLM(
             RedBearModelConfig(
                 model_name=model_name,
                 provider=provider,            
                 api_key=api_key,
                 base_url=api_base,
-                extra_params={"streaming": stream}
+                extra_params=extra_params
             ), 
             type=model_type
         )
+        
+        logger.debug(f"创建 LLM 实例: provider={provider}, model={model_name}, streaming={stream}")
+        
         return llm, prompt_or_messages
     
     async def execute(self, state: WorkflowState) -> AIMessage:
@@ -201,47 +204,54 @@ class LLMNode(BaseNode):
                 }
         return None
     
-    # async def execute_stream(self, state: WorkflowState):
-    #     """流式执行 LLM 调用
+    async def execute_stream(self, state: WorkflowState):
+        """流式执行 LLM 调用
         
-    #     Args:
-    #         state: 工作流状态
+        Args:
+            state: 工作流状态
         
-    #     Yields:
-    #         文本片段（chunk）或完成标记
-    #     """
-    #     llm, prompt_or_messages = self._prepare_llm(state,True)
+        Yields:
+            文本片段（chunk）或完成标记
+        """
+        llm, prompt_or_messages = self._prepare_llm(state, True)
         
-    #     logger.info(f"节点 {self.node_id} 开始执行 LLM 调用（流式）")
+        logger.info(f"节点 {self.node_id} 开始执行 LLM 调用（流式）")
+        logger.debug(f"LLM 配置: streaming={getattr(llm._model, 'streaming', 'unknown')}")
         
-    #     # 累积完整响应
-    #     full_response = ""
-    #     last_chunk = None
+        # 累积完整响应
+        full_response = ""
+        last_chunk = None
+        chunk_count = 0
         
-    #     # 调用 LLM（流式，支持字符串或消息列表）
-    #     async for chunk in llm.astream(prompt_or_messages):
-    #         # 提取内容
-    #         if hasattr(chunk, 'content'):
-    #             content = chunk.content
-    #         else:
-    #             content = str(chunk)
+        # 调用 LLM（流式，支持字符串或消息列表）
+        # 注意：astream 方法本身就是流式的，不需要额外配置
+        async for chunk in llm.astream(prompt_or_messages):
+            # 提取内容
+            if hasattr(chunk, 'content'):
+                content = chunk.content
+            else:
+                content = str(chunk)
             
-    #         full_response += content
-    #         last_chunk = chunk
-    #         logger.info(f"节点 {self.node_id} LLM : {content}")
-    #         # 流式返回每个文本片段
-    #         yield content
+            # 只有当内容不为空时才处理
+            if content:
+                full_response += content
+                last_chunk = chunk
+                chunk_count += 1
+                
+                # logger.debug(f"节点 {self.node_id} LLM chunk #{chunk_count}: {content[:50]}...")
+                # 流式返回每个文本片段
+                yield content #AIMessage(content=content)
         
-    #     logger.info(f"节点 {self.node_id} LLM 调用完成，输出长度: {len(full_response)}")
+        logger.info(f"节点 {self.node_id} LLM 调用完成，输出长度: {len(full_response)}, 总 chunks: {chunk_count}")
         
-    #     # 构建完整的 AIMessage（包含元数据）
-    #     if isinstance(last_chunk, AIMessage):
-    #         final_message = AIMessage(
-    #             content=full_response,
-    #             response_metadata=last_chunk.response_metadata if hasattr(last_chunk, 'response_metadata') else {}
-    #         )
-    #     else:
-    #         final_message = AIMessage(content=full_response)
+        # 构建完整的 AIMessage（包含元数据）
+        if isinstance(last_chunk, AIMessage):
+            final_message = AIMessage(
+                content=full_response,
+                response_metadata=last_chunk.response_metadata if hasattr(last_chunk, 'response_metadata') else {}
+            )
+        else:
+            final_message = AIMessage(content=full_response)
         
-    #     # yield 完成标记
-    #     yield {"__final__": True, "result": final_message}
+        # yield 完成标记
+        yield {"__final__": True, "result": final_message}